In [1]:
!pip install yfinance
!pip install bs4
!pip install nbformat
!pip install --upgrade plotly
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In [2]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [3]:
import plotly.io as pio
pio.renderers.default = "iframe"
In [4]:
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)
In [5]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()
    from IPython.display import display, HTML
    fig_html = fig.to_html()
    display(HTML(fig_html))
In [6]:
tesla  = yf.Ticker("TSLA")
tesla
Out[6]:
yfinance.Ticker object <TSLA>
In [7]:
tesla_data = tesla.history(period="max")
In [8]:
tesla_data.reset_index(inplace=True)
In [9]:
tesla_data.head()
Out[9]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 281494500 0.0 0.0
1 2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 257806500 0.0 0.0
2 2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 123282000 0.0 0.0
3 2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 77097000 0.0 0.0
4 2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 103003500 0.0 0.0
In [10]:
url =  "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"
In [11]:
html_data = requests.get(url).text
In [12]:
soup = BeautifulSoup(html_data, 'html.parser')
soup
Out[12]:
<!DOCTYPE html>

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<link href="https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue" rel="canonical"/>
<title>Tesla Revenue 2010-2022 | TSLA | MacroTrends</title>
<meta content="Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services.  Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.    
				
				&lt;ul style='margin-top:10px;'&gt;
				&lt;li&gt;Tesla revenue for the quarter ending September 30, 2022 was &lt;strong&gt;$21.454B&lt;/strong&gt;, a &lt;strong&gt;55.95% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla revenue for the twelve months ending September 30, 2022 was &lt;strong&gt;$74.863B&lt;/strong&gt;, a &lt;strong&gt;59.8% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2021 was &lt;strong&gt;$53.823B&lt;/strong&gt;, a &lt;strong&gt;70.67% increase&lt;/strong&gt; from 2020.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2020 was &lt;strong&gt;$31.536B&lt;/strong&gt;, a &lt;strong&gt;28.31% increase&lt;/strong&gt; from 2019.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2019 was &lt;strong&gt;$24.578B&lt;/strong&gt;, a &lt;strong&gt;14.52% increase&lt;/strong&gt; from 2018.&lt;/li&gt;
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<meta content="Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services.  Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.    
				
				&lt;ul style='margin-top:10px;'&gt;
				&lt;li&gt;Tesla revenue for the quarter ending September 30, 2022 was &lt;strong&gt;$21.454B&lt;/strong&gt;, a &lt;strong&gt;55.95% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla revenue for the twelve months ending September 30, 2022 was &lt;strong&gt;$74.863B&lt;/strong&gt;, a &lt;strong&gt;59.8% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2021 was &lt;strong&gt;$53.823B&lt;/strong&gt;, a &lt;strong&gt;70.67% increase&lt;/strong&gt; from 2020.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2020 was &lt;strong&gt;$31.536B&lt;/strong&gt;, a &lt;strong&gt;28.31% increase&lt;/strong&gt; from 2019.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2019 was &lt;strong&gt;$24.578B&lt;/strong&gt;, a &lt;strong&gt;14.52% increase&lt;/strong&gt; from 2018.&lt;/li&gt;
				&lt;/ul&gt;" name="twitter:description"/>
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<meta content="Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services.  Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.    
				
				&lt;ul style='margin-top:10px;'&gt;
				&lt;li&gt;Tesla revenue for the quarter ending September 30, 2022 was &lt;strong&gt;$21.454B&lt;/strong&gt;, a &lt;strong&gt;55.95% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla revenue for the twelve months ending September 30, 2022 was &lt;strong&gt;$74.863B&lt;/strong&gt;, a &lt;strong&gt;59.8% increase&lt;/strong&gt; year-over-year.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2021 was &lt;strong&gt;$53.823B&lt;/strong&gt;, a &lt;strong&gt;70.67% increase&lt;/strong&gt; from 2020.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2020 was &lt;strong&gt;$31.536B&lt;/strong&gt;, a &lt;strong&gt;28.31% increase&lt;/strong&gt; from 2019.&lt;/li&gt;
				&lt;li&gt;Tesla annual revenue for 2019 was &lt;strong&gt;$24.578B&lt;/strong&gt;, a &lt;strong&gt;14.52% increase&lt;/strong&gt; from 2018.&lt;/li&gt;
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<h2 style="margin-left:0px; font-weight:600; color:#444;">Tesla Revenue 2010-2022 | TSLA</h2>
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<span style="color:#444; line-height: 1.8;">Tesla annual/quarterly revenue history and growth rate from 2010 to 2022. Revenue can be defined as the amount of money a company receives from its customers in exchange for the sales of goods or services.  Revenue is the top line item on an income statement from which all costs and expenses are subtracted to arrive at net income.    
				
				<ul style="margin-top:10px;">
<li>Tesla revenue for the quarter ending September 30, 2022 was <strong>$21.454B</strong>, a <strong>55.95% increase</strong> year-over-year.</li>
<li>Tesla revenue for the twelve months ending September 30, 2022 was <strong>$74.863B</strong>, a <strong>59.8% increase</strong> year-over-year.</li>
<li>Tesla annual revenue for 2021 was <strong>$53.823B</strong>, a <strong>70.67% increase</strong> from 2020.</li>
<li>Tesla annual revenue for 2020 was <strong>$31.536B</strong>, a <strong>28.31% increase</strong> from 2019.</li>
<li>Tesla annual revenue for 2019 was <strong>$24.578B</strong>, a <strong>14.52% increase</strong> from 2018.</li>
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<th colspan="2" style="text-align:center">Tesla Annual Revenue<br/><span style="font-size:14px;">(Millions of US $)</span></th>
</tr>
</thead>
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<tr>
<td style="text-align:center">2021</td>
<td style="text-align:center">$53,823</td>
</tr>
<tr>
<td style="text-align:center">2020</td>
<td style="text-align:center">$31,536</td>
</tr>
<tr>
<td style="text-align:center">2019</td>
<td style="text-align:center">$24,578</td>
</tr>
<tr>
<td style="text-align:center">2018</td>
<td style="text-align:center">$21,461</td>
</tr>
<tr>
<td style="text-align:center">2017</td>
<td style="text-align:center">$11,759</td>
</tr>
<tr>
<td style="text-align:center">2016</td>
<td style="text-align:center">$7,000</td>
</tr>
<tr>
<td style="text-align:center">2015</td>
<td style="text-align:center">$4,046</td>
</tr>
<tr>
<td style="text-align:center">2014</td>
<td style="text-align:center">$3,198</td>
</tr>
<tr>
<td style="text-align:center">2013</td>
<td style="text-align:center">$2,013</td>
</tr>
<tr>
<td style="text-align:center">2012</td>
<td style="text-align:center">$413</td>
</tr>
<tr>
<td style="text-align:center">2011</td>
<td style="text-align:center">$204</td>
</tr>
<tr>
<td style="text-align:center">2010</td>
<td style="text-align:center">$117</td>
</tr>
<tr>
<td style="text-align:center">2009</td>
<td style="text-align:center">$112</td>
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<th colspan="2" style="text-align:center">Tesla Quarterly Revenue<br/><span style="font-size:14px;">(Millions of US $)</span></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">2022-09-30</td>
<td style="text-align:center">$21,454</td>
</tr>
<tr>
<td style="text-align:center">2022-06-30</td>
<td style="text-align:center">$16,934</td>
</tr>
<tr>
<td style="text-align:center">2022-03-31</td>
<td style="text-align:center">$18,756</td>
</tr>
<tr>
<td style="text-align:center">2021-12-31</td>
<td style="text-align:center">$17,719</td>
</tr>
<tr>
<td style="text-align:center">2021-09-30</td>
<td style="text-align:center">$13,757</td>
</tr>
<tr>
<td style="text-align:center">2021-06-30</td>
<td style="text-align:center">$11,958</td>
</tr>
<tr>
<td style="text-align:center">2021-03-31</td>
<td style="text-align:center">$10,389</td>
</tr>
<tr>
<td style="text-align:center">2020-12-31</td>
<td style="text-align:center">$10,744</td>
</tr>
<tr>
<td style="text-align:center">2020-09-30</td>
<td style="text-align:center">$8,771</td>
</tr>
<tr>
<td style="text-align:center">2020-06-30</td>
<td style="text-align:center">$6,036</td>
</tr>
<tr>
<td style="text-align:center">2020-03-31</td>
<td style="text-align:center">$5,985</td>
</tr>
<tr>
<td style="text-align:center">2019-12-31</td>
<td style="text-align:center">$7,384</td>
</tr>
<tr>
<td style="text-align:center">2019-09-30</td>
<td style="text-align:center">$6,303</td>
</tr>
<tr>
<td style="text-align:center">2019-06-30</td>
<td style="text-align:center">$6,350</td>
</tr>
<tr>
<td style="text-align:center">2019-03-31</td>
<td style="text-align:center">$4,541</td>
</tr>
<tr>
<td style="text-align:center">2018-12-31</td>
<td style="text-align:center">$7,226</td>
</tr>
<tr>
<td style="text-align:center">2018-09-30</td>
<td style="text-align:center">$6,824</td>
</tr>
<tr>
<td style="text-align:center">2018-06-30</td>
<td style="text-align:center">$4,002</td>
</tr>
<tr>
<td style="text-align:center">2018-03-31</td>
<td style="text-align:center">$3,409</td>
</tr>
<tr>
<td style="text-align:center">2017-12-31</td>
<td style="text-align:center">$3,288</td>
</tr>
<tr>
<td style="text-align:center">2017-09-30</td>
<td style="text-align:center">$2,985</td>
</tr>
<tr>
<td style="text-align:center">2017-06-30</td>
<td style="text-align:center">$2,790</td>
</tr>
<tr>
<td style="text-align:center">2017-03-31</td>
<td style="text-align:center">$2,696</td>
</tr>
<tr>
<td style="text-align:center">2016-12-31</td>
<td style="text-align:center">$2,285</td>
</tr>
<tr>
<td style="text-align:center">2016-09-30</td>
<td style="text-align:center">$2,298</td>
</tr>
<tr>
<td style="text-align:center">2016-06-30</td>
<td style="text-align:center">$1,270</td>
</tr>
<tr>
<td style="text-align:center">2016-03-31</td>
<td style="text-align:center">$1,147</td>
</tr>
<tr>
<td style="text-align:center">2015-12-31</td>
<td style="text-align:center">$1,214</td>
</tr>
<tr>
<td style="text-align:center">2015-09-30</td>
<td style="text-align:center">$937</td>
</tr>
<tr>
<td style="text-align:center">2015-06-30</td>
<td style="text-align:center">$955</td>
</tr>
<tr>
<td style="text-align:center">2015-03-31</td>
<td style="text-align:center">$940</td>
</tr>
<tr>
<td style="text-align:center">2014-12-31</td>
<td style="text-align:center">$957</td>
</tr>
<tr>
<td style="text-align:center">2014-09-30</td>
<td style="text-align:center">$852</td>
</tr>
<tr>
<td style="text-align:center">2014-06-30</td>
<td style="text-align:center">$769</td>
</tr>
<tr>
<td style="text-align:center">2014-03-31</td>
<td style="text-align:center">$621</td>
</tr>
<tr>
<td style="text-align:center">2013-12-31</td>
<td style="text-align:center">$615</td>
</tr>
<tr>
<td style="text-align:center">2013-09-30</td>
<td style="text-align:center">$431</td>
</tr>
<tr>
<td style="text-align:center">2013-06-30</td>
<td style="text-align:center">$405</td>
</tr>
<tr>
<td style="text-align:center">2013-03-31</td>
<td style="text-align:center">$562</td>
</tr>
<tr>
<td style="text-align:center">2012-12-31</td>
<td style="text-align:center">$306</td>
</tr>
<tr>
<td style="text-align:center">2012-09-30</td>
<td style="text-align:center">$50</td>
</tr>
<tr>
<td style="text-align:center">2012-06-30</td>
<td style="text-align:center">$27</td>
</tr>
<tr>
<td style="text-align:center">2012-03-31</td>
<td style="text-align:center">$30</td>
</tr>
<tr>
<td style="text-align:center">2011-12-31</td>
<td style="text-align:center">$39</td>
</tr>
<tr>
<td style="text-align:center">2011-09-30</td>
<td style="text-align:center">$58</td>
</tr>
<tr>
<td style="text-align:center">2011-06-30</td>
<td style="text-align:center">$58</td>
</tr>
<tr>
<td style="text-align:center">2011-03-31</td>
<td style="text-align:center">$49</td>
</tr>
<tr>
<td style="text-align:center">2010-12-31</td>
<td style="text-align:center">$36</td>
</tr>
<tr>
<td style="text-align:center">2010-09-30</td>
<td style="text-align:center">$31</td>
</tr>
<tr>
<td style="text-align:center">2010-06-30</td>
<td style="text-align:center">$28</td>
</tr>
<tr>
<td style="text-align:center">2010-03-31</td>
<td style="text-align:center">$21</td>
</tr>
<tr>
<td style="text-align:center">2009-12-31</td>
<td style="text-align:center"></td>
</tr>
<tr>
<td style="text-align:center">2009-09-30</td>
<td style="text-align:center">$46</td>
</tr>
<tr>
<td style="text-align:center">2009-06-30</td>
<td style="text-align:center">$27</td>
</tr>
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<th style="text-align:center">Sector</th>
<th style="text-align:center">Industry</th>
<th style="text-align:center">Market Cap</th>
<th style="text-align:center">Revenue</th>
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<td style="text-align:center"><a href="https://www.macrotrends.net/stocks/sector/5/auto-tires-trucks">Auto/Tires/Trucks</a></td>
<td style="text-align:center"><a href="https://www.macrotrends.net/stocks/industry/7/">Auto Manufacturers - Domestic</a></td>
<td style="text-align:center">$549.575B</td>
<td style="text-align:center">$53.823B</td>
</tr>
<tr>
<td colspan="4" style="padding:15px;">
<span>Tesla is the market leader in battery-powered electric car sales in the United States, with roughly 70% market share. The company's flagship Model 3 is the best-selling EV model in the United States. Tesla, which has managed to garner the reputation of a gold standard over the years, is now a far bigger entity that what it started off since its IPO in 2010, with its market cap crossing $1 trillion for the first time in October 2021.? The EV king's market capitalization is more than the combined value of legacy automakers including Toyota, Volkswagen, Daimler, General Motors and Ford.Over the years, Tesla has shifted from developing niche products for affluent buyers to making more affordable EVs for the masses. The firm's three-pronged business model approach of direct sales, servicing, and charging its EVs sets it apart from other carmakers. Tesla, which is touted as the clean energy revolutionary automaker, is much more than just a car manufacturer.</span>
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<th style="text-align:center; width:20%;">PE Ratio</th>
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<td style="text-align:left"><a href="/stocks/charts/GM/general-motors/revenue">General Motors (GM)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$53.930B</td>
<td style="text-align:center">5.56</td>
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<td style="text-align:left"><a href="/stocks/charts/F/ford-motor/revenue">Ford Motor (F)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$52.668B</td>
<td style="text-align:center">8.09</td>
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<td style="text-align:left"><a href="/stocks/charts/HOG/harley-davidson/revenue">Harley-Davidson (HOG)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$6.762B</td>
<td style="text-align:center">9.56</td>
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<td style="text-align:left"><a href="/stocks/charts/PII/polaris/revenue">Polaris (PII)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$6.267B</td>
<td style="text-align:center">11.86</td>
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<td style="text-align:left"><a href="/stocks/charts/IAA/iaa/revenue">IAA (IAA)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$5.134B</td>
<td style="text-align:center">16.40</td>
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<td style="text-align:left"><a href="/stocks/charts/FSR/fisker/revenue">Fisker (FSR)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$2.261B</td>
<td style="text-align:center">0.00</td>
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<td style="text-align:left"><a href="/stocks/charts/LEV/lion-electric/revenue">Lion Electric (LEV)</a></td>
<td style="text-align:center">Canada</td>
<td style="text-align:center">$0.551B</td>
<td style="text-align:center">0.00</td>
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<td style="text-align:left"><a href="/stocks/charts/VLTA/volta/revenue">Volta (VLTA)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$0.071B</td>
<td style="text-align:center">0.00</td>
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<td style="text-align:left"><a href="/stocks/charts/BRDS/bird-global/revenue">Bird Global (BRDS)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$0.054B</td>
<td style="text-align:center">0.00</td>
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<td style="text-align:left"><a href="/stocks/charts/ZEV/lightning-emotors/revenue">Lightning EMotors (ZEV)</a></td>
<td style="text-align:center">United States</td>
<td style="text-align:center">$0.043B</td>
<td style="text-align:center">0.00</td>
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In [13]:
table = soup.find_all('tbody')[1]
In [14]:
tesla_list = []
for variable in table.find_all("tr"):
    columns = variable.find_all("td")
    date = columns[0].text
    revenue = columns[1].text
    tesla_list.append({"Date": date, "Revenue": revenue})

tesla_revenue = pd.DataFrame(tesla_list)
tesla_revenue
Out[14]:
Date Revenue
0 2022-09-30 $21,454
1 2022-06-30 $16,934
2 2022-03-31 $18,756
3 2021-12-31 $17,719
4 2021-09-30 $13,757
5 2021-06-30 $11,958
6 2021-03-31 $10,389
7 2020-12-31 $10,744
8 2020-09-30 $8,771
9 2020-06-30 $6,036
10 2020-03-31 $5,985
11 2019-12-31 $7,384
12 2019-09-30 $6,303
13 2019-06-30 $6,350
14 2019-03-31 $4,541
15 2018-12-31 $7,226
16 2018-09-30 $6,824
17 2018-06-30 $4,002
18 2018-03-31 $3,409
19 2017-12-31 $3,288
20 2017-09-30 $2,985
21 2017-06-30 $2,790
22 2017-03-31 $2,696
23 2016-12-31 $2,285
24 2016-09-30 $2,298
25 2016-06-30 $1,270
26 2016-03-31 $1,147
27 2015-12-31 $1,214
28 2015-09-30 $937
29 2015-06-30 $955
30 2015-03-31 $940
31 2014-12-31 $957
32 2014-09-30 $852
33 2014-06-30 $769
34 2014-03-31 $621
35 2013-12-31 $615
36 2013-09-30 $431
37 2013-06-30 $405
38 2013-03-31 $562
39 2012-12-31 $306
40 2012-09-30 $50
41 2012-06-30 $27
42 2012-03-31 $30
43 2011-12-31 $39
44 2011-09-30 $58
45 2011-06-30 $58
46 2011-03-31 $49
47 2010-12-31 $36
48 2010-09-30 $31
49 2010-06-30 $28
50 2010-03-31 $21
51 2009-12-31
52 2009-09-30 $46
53 2009-06-30 $27
In [15]:
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"",regex=True)
<>:1: SyntaxWarning:

invalid escape sequence '\$'

<>:1: SyntaxWarning:

invalid escape sequence '\$'

/tmp/ipython-input-3152827612.py:1: SyntaxWarning:

invalid escape sequence '\$'

In [16]:
tesla_revenue.dropna(inplace=True)

tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
tesla_revenue
Out[16]:
Date Revenue
0 2022-09-30 21454
1 2022-06-30 16934
2 2022-03-31 18756
3 2021-12-31 17719
4 2021-09-30 13757
5 2021-06-30 11958
6 2021-03-31 10389
7 2020-12-31 10744
8 2020-09-30 8771
9 2020-06-30 6036
10 2020-03-31 5985
11 2019-12-31 7384
12 2019-09-30 6303
13 2019-06-30 6350
14 2019-03-31 4541
15 2018-12-31 7226
16 2018-09-30 6824
17 2018-06-30 4002
18 2018-03-31 3409
19 2017-12-31 3288
20 2017-09-30 2985
21 2017-06-30 2790
22 2017-03-31 2696
23 2016-12-31 2285
24 2016-09-30 2298
25 2016-06-30 1270
26 2016-03-31 1147
27 2015-12-31 1214
28 2015-09-30 937
29 2015-06-30 955
30 2015-03-31 940
31 2014-12-31 957
32 2014-09-30 852
33 2014-06-30 769
34 2014-03-31 621
35 2013-12-31 615
36 2013-09-30 431
37 2013-06-30 405
38 2013-03-31 562
39 2012-12-31 306
40 2012-09-30 50
41 2012-06-30 27
42 2012-03-31 30
43 2011-12-31 39
44 2011-09-30 58
45 2011-06-30 58
46 2011-03-31 49
47 2010-12-31 36
48 2010-09-30 31
49 2010-06-30 28
50 2010-03-31 21
52 2009-09-30 46
53 2009-06-30 27
In [17]:
tesla_revenue.tail()
Out[17]:
Date Revenue
48 2010-09-30 31
49 2010-06-30 28
50 2010-03-31 21
52 2009-09-30 46
53 2009-06-30 27
In [18]:
gamestop = yf.Ticker("GME")
gamestop
Out[18]:
yfinance.Ticker object <GME>
In [19]:
gme_data = gamestop.history(period="max")
gme_data
Out[19]:
Open High Low Close Volume Dividends Stock Splits
Date
2002-02-13 00:00:00-05:00 1.620128 1.693350 1.603296 1.691667 76216000 0.0 0.0
2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683251 11021600 0.0 0.0
2002-02-15 00:00:00-05:00 1.683250 1.687458 1.658002 1.674834 8389600 0.0 0.0
2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0
... ... ... ... ... ... ... ...
2025-09-17 00:00:00-04:00 26.350000 26.469999 25.580000 26.100000 7897100 0.0 0.0
2025-09-18 00:00:00-04:00 26.209999 26.420000 25.840000 25.879999 7806200 0.0 0.0
2025-09-19 00:00:00-04:00 25.830000 26.389999 25.790001 26.080000 9567600 0.0 0.0
2025-09-22 00:00:00-04:00 26.000000 27.340000 25.969999 26.780001 11263600 0.0 0.0
2025-09-23 00:00:00-04:00 26.905001 27.240000 26.590000 26.799999 6638965 0.0 0.0

5941 rows × 7 columns

In [20]:
gme_data.reset_index(inplace=True)
gme_data.head()
Out[20]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 00:00:00-05:00 1.620128 1.693350 1.603296 1.691667 76216000 0.0 0.0
1 2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683251 11021600 0.0 0.0
2 2002-02-15 00:00:00-05:00 1.683250 1.687458 1.658002 1.674834 8389600 0.0 0.0
3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0
In [21]:
url2 = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
html_data_2 = requests.get(url2).text
In [22]:
soup2 = BeautifulSoup(html_data_2, 'html.parser')
In [23]:
table1 = soup2.find_all('tbody')[1]
table1
Out[23]:
<tbody>
<tr>
<td style="text-align:center">2020-04-30</td>
<td style="text-align:center">$1,021</td>
</tr>
<tr>
<td style="text-align:center">2020-01-31</td>
<td style="text-align:center">$2,194</td>
</tr>
<tr>
<td style="text-align:center">2019-10-31</td>
<td style="text-align:center">$1,439</td>
</tr>
<tr>
<td style="text-align:center">2019-07-31</td>
<td style="text-align:center">$1,286</td>
</tr>
<tr>
<td style="text-align:center">2019-04-30</td>
<td style="text-align:center">$1,548</td>
</tr>
<tr>
<td style="text-align:center">2019-01-31</td>
<td style="text-align:center">$3,063</td>
</tr>
<tr>
<td style="text-align:center">2018-10-31</td>
<td style="text-align:center">$1,935</td>
</tr>
<tr>
<td style="text-align:center">2018-07-31</td>
<td style="text-align:center">$1,501</td>
</tr>
<tr>
<td style="text-align:center">2018-04-30</td>
<td style="text-align:center">$1,786</td>
</tr>
<tr>
<td style="text-align:center">2018-01-31</td>
<td style="text-align:center">$2,825</td>
</tr>
<tr>
<td style="text-align:center">2017-10-31</td>
<td style="text-align:center">$1,989</td>
</tr>
<tr>
<td style="text-align:center">2017-07-31</td>
<td style="text-align:center">$1,688</td>
</tr>
<tr>
<td style="text-align:center">2017-04-30</td>
<td style="text-align:center">$2,046</td>
</tr>
<tr>
<td style="text-align:center">2017-01-31</td>
<td style="text-align:center">$2,403</td>
</tr>
<tr>
<td style="text-align:center">2016-10-31</td>
<td style="text-align:center">$1,959</td>
</tr>
<tr>
<td style="text-align:center">2016-07-31</td>
<td style="text-align:center">$1,632</td>
</tr>
<tr>
<td style="text-align:center">2016-04-30</td>
<td style="text-align:center">$1,972</td>
</tr>
<tr>
<td style="text-align:center">2016-01-31</td>
<td style="text-align:center">$3,525</td>
</tr>
<tr>
<td style="text-align:center">2015-10-31</td>
<td style="text-align:center">$2,016</td>
</tr>
<tr>
<td style="text-align:center">2015-07-31</td>
<td style="text-align:center">$1,762</td>
</tr>
<tr>
<td style="text-align:center">2015-04-30</td>
<td style="text-align:center">$2,061</td>
</tr>
<tr>
<td style="text-align:center">2015-01-31</td>
<td style="text-align:center">$3,476</td>
</tr>
<tr>
<td style="text-align:center">2014-10-31</td>
<td style="text-align:center">$2,092</td>
</tr>
<tr>
<td style="text-align:center">2014-07-31</td>
<td style="text-align:center">$1,731</td>
</tr>
<tr>
<td style="text-align:center">2014-04-30</td>
<td style="text-align:center">$1,996</td>
</tr>
<tr>
<td style="text-align:center">2014-01-31</td>
<td style="text-align:center">$3,684</td>
</tr>
<tr>
<td style="text-align:center">2013-10-31</td>
<td style="text-align:center">$2,107</td>
</tr>
<tr>
<td style="text-align:center">2013-07-31</td>
<td style="text-align:center">$1,384</td>
</tr>
<tr>
<td style="text-align:center">2013-04-30</td>
<td style="text-align:center">$1,865</td>
</tr>
<tr>
<td style="text-align:center">2013-01-31</td>
<td style="text-align:center">$3,562</td>
</tr>
<tr>
<td style="text-align:center">2012-10-31</td>
<td style="text-align:center">$1,773</td>
</tr>
<tr>
<td style="text-align:center">2012-07-31</td>
<td style="text-align:center">$1,550</td>
</tr>
<tr>
<td style="text-align:center">2012-04-30</td>
<td style="text-align:center">$2,002</td>
</tr>
<tr>
<td style="text-align:center">2012-01-31</td>
<td style="text-align:center">$3,579</td>
</tr>
<tr>
<td style="text-align:center">2011-10-31</td>
<td style="text-align:center">$1,947</td>
</tr>
<tr>
<td style="text-align:center">2011-07-31</td>
<td style="text-align:center">$1,744</td>
</tr>
<tr>
<td style="text-align:center">2011-04-30</td>
<td style="text-align:center">$2,281</td>
</tr>
<tr>
<td style="text-align:center">2011-01-31</td>
<td style="text-align:center">$3,693</td>
</tr>
<tr>
<td style="text-align:center">2010-10-31</td>
<td style="text-align:center">$1,899</td>
</tr>
<tr>
<td style="text-align:center">2010-07-31</td>
<td style="text-align:center">$1,799</td>
</tr>
<tr>
<td style="text-align:center">2010-04-30</td>
<td style="text-align:center">$2,083</td>
</tr>
<tr>
<td style="text-align:center">2010-01-31</td>
<td style="text-align:center">$3,524</td>
</tr>
<tr>
<td style="text-align:center">2009-10-31</td>
<td style="text-align:center">$1,835</td>
</tr>
<tr>
<td style="text-align:center">2009-07-31</td>
<td style="text-align:center">$1,739</td>
</tr>
<tr>
<td style="text-align:center">2009-04-30</td>
<td style="text-align:center">$1,981</td>
</tr>
<tr>
<td style="text-align:center">2009-01-31</td>
<td style="text-align:center">$3,492</td>
</tr>
<tr>
<td style="text-align:center">2008-10-31</td>
<td style="text-align:center">$1,696</td>
</tr>
<tr>
<td style="text-align:center">2008-07-31</td>
<td style="text-align:center">$1,804</td>
</tr>
<tr>
<td style="text-align:center">2008-04-30</td>
<td style="text-align:center">$1,814</td>
</tr>
<tr>
<td style="text-align:center">2008-01-31</td>
<td style="text-align:center">$2,866</td>
</tr>
<tr>
<td style="text-align:center">2007-10-31</td>
<td style="text-align:center">$1,611</td>
</tr>
<tr>
<td style="text-align:center">2007-07-31</td>
<td style="text-align:center">$1,338</td>
</tr>
<tr>
<td style="text-align:center">2007-04-30</td>
<td style="text-align:center">$1,279</td>
</tr>
<tr>
<td style="text-align:center">2007-01-31</td>
<td style="text-align:center">$2,304</td>
</tr>
<tr>
<td style="text-align:center">2006-10-31</td>
<td style="text-align:center">$1,012</td>
</tr>
<tr>
<td style="text-align:center">2006-07-31</td>
<td style="text-align:center">$963</td>
</tr>
<tr>
<td style="text-align:center">2006-04-30</td>
<td style="text-align:center">$1,040</td>
</tr>
<tr>
<td style="text-align:center">2006-01-31</td>
<td style="text-align:center">$1,667</td>
</tr>
<tr>
<td style="text-align:center">2005-10-31</td>
<td style="text-align:center">$534</td>
</tr>
<tr>
<td style="text-align:center">2005-07-31</td>
<td style="text-align:center">$416</td>
</tr>
<tr>
<td style="text-align:center">2005-04-30</td>
<td style="text-align:center">$475</td>
</tr>
<tr>
<td style="text-align:center">2005-01-31</td>
<td style="text-align:center">$709</td>
</tr>
</tbody>
In [24]:
gme_list = []
for variable in table1.find_all("tr"):
    column = variable.find_all("td")
    date = column[0].text
    revenue = column[1].text
    gme_list.append({"Date":date, "Revenue":revenue})

gme_revenue = pd.DataFrame(gme_list)
gme_revenue
Out[24]:
Date Revenue
0 2020-04-30 $1,021
1 2020-01-31 $2,194
2 2019-10-31 $1,439
3 2019-07-31 $1,286
4 2019-04-30 $1,548
... ... ...
57 2006-01-31 $1,667
58 2005-10-31 $534
59 2005-07-31 $416
60 2005-04-30 $475
61 2005-01-31 $709

62 rows × 2 columns

In [25]:
gme_revenue["Revenue"] = gme_revenue["Revenue"].str.replace(",", "").str.replace("$", "")
gme_revenue
Out[25]:
Date Revenue
0 2020-04-30 1021
1 2020-01-31 2194
2 2019-10-31 1439
3 2019-07-31 1286
4 2019-04-30 1548
... ... ...
57 2006-01-31 1667
58 2005-10-31 534
59 2005-07-31 416
60 2005-04-30 475
61 2005-01-31 709

62 rows × 2 columns

In [26]:
gme_revenue.dropna(inplace=True)

gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]
gme_revenue
Out[26]:
Date Revenue
0 2020-04-30 1021
1 2020-01-31 2194
2 2019-10-31 1439
3 2019-07-31 1286
4 2019-04-30 1548
... ... ...
57 2006-01-31 1667
58 2005-10-31 534
59 2005-07-31 416
60 2005-04-30 475
61 2005-01-31 709

62 rows × 2 columns

In [27]:
gme_revenue.tail()
Out[27]:
Date Revenue
57 2006-01-31 1667
58 2005-10-31 534
59 2005-07-31 416
60 2005-04-30 475
61 2005-01-31 709
In [28]:
gme_data
Out[28]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 00:00:00-05:00 1.620128 1.693350 1.603296 1.691667 76216000 0.0 0.0
1 2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683251 11021600 0.0 0.0
2 2002-02-15 00:00:00-05:00 1.683250 1.687458 1.658002 1.674834 8389600 0.0 0.0
3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0
... ... ... ... ... ... ... ... ...
5936 2025-09-17 00:00:00-04:00 26.350000 26.469999 25.580000 26.100000 7897100 0.0 0.0
5937 2025-09-18 00:00:00-04:00 26.209999 26.420000 25.840000 25.879999 7806200 0.0 0.0
5938 2025-09-19 00:00:00-04:00 25.830000 26.389999 25.790001 26.080000 9567600 0.0 0.0
5939 2025-09-22 00:00:00-04:00 26.000000 27.340000 25.969999 26.780001 11263600 0.0 0.0
5940 2025-09-23 00:00:00-04:00 26.905001 27.240000 26.590000 26.799999 6638965 0.0 0.0

5941 rows × 8 columns

In [29]:
gme_revenue
Out[29]:
Date Revenue
0 2020-04-30 1021
1 2020-01-31 2194
2 2019-10-31 1439
3 2019-07-31 1286
4 2019-04-30 1548
... ... ...
57 2006-01-31 1667
58 2005-10-31 534
59 2005-07-31 416
60 2005-04-30 475
61 2005-01-31 709

62 rows × 2 columns

In [30]:
make_graph(tesla_data, tesla_revenue, "Tesla")
/tmp/ipython-input-109047474.py:5: UserWarning:

The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.

/tmp/ipython-input-109047474.py:6: UserWarning:

The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.

In [31]:
make_graph(gme_data, gme_revenue, "GameStop")
/tmp/ipython-input-109047474.py:5: UserWarning:

The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.

/tmp/ipython-input-109047474.py:6: UserWarning:

The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.